Overview

Dataset statistics

Number of variables11
Number of observations20433
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory114.9 B

Variable types

Numeric9
Categorical2

Reproduction

Analysis started2024-05-30 21:45:22.834382
Analysis finished2024-05-30 21:45:28.802289
Duration5.97 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

longitude
Real number (ℝ)

Distinct844
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.57069
Minimum-124.35
Maximum-114.31
Zeros0
Zeros (%)0.0%
Negative20433
Negative (%)100.0%
Memory size835.3 KiB
2024-05-30T17:45:28.870015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-124.35
5-th percentile-122.47
Q1-121.8
median-118.49
Q3-118.01
95-th percentile-117.08
Maximum-114.31
Range10.04
Interquartile range (IQR)3.79

Descriptive statistics

Standard deviation2.0035779
Coefficient of variation (CV)-0.01675643
Kurtosis-1.3325482
Mean-119.57069
Median Absolute Deviation (MAD)1.29
Skewness-0.2961409
Sum-2443187.9
Variance4.0143244
MonotonicityNot monotonic
2024-05-30T17:45:28.946336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.31 159
 
0.8%
-118.3 157
 
0.8%
-118.29 146
 
0.7%
-118.27 141
 
0.7%
-118.32 141
 
0.7%
-118.28 139
 
0.7%
-118.35 138
 
0.7%
-118.36 135
 
0.7%
-118.19 134
 
0.7%
-118.37 126
 
0.6%
Other values (834) 19017
93.1%
ValueCountFrequency (%)
-124.35 1
 
< 0.1%
-124.3 2
 
< 0.1%
-124.27 1
 
< 0.1%
-124.26 1
 
< 0.1%
-124.25 1
 
< 0.1%
-124.23 3
< 0.1%
-124.22 1
 
< 0.1%
-124.21 3
< 0.1%
-124.19 4
< 0.1%
-124.18 6
< 0.1%
ValueCountFrequency (%)
-114.31 1
 
< 0.1%
-114.47 1
 
< 0.1%
-114.49 1
 
< 0.1%
-114.55 1
 
< 0.1%
-114.56 1
 
< 0.1%
-114.57 3
< 0.1%
-114.58 2
< 0.1%
-114.59 1
 
< 0.1%
-114.6 3
< 0.1%
-114.61 3
< 0.1%

latitude
Real number (ℝ)

Distinct861
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.633221
Minimum32.54
Maximum41.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2024-05-30T17:45:29.041093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum32.54
5-th percentile32.82
Q133.93
median34.26
Q337.72
95-th percentile38.96
Maximum41.95
Range9.41
Interquartile range (IQR)3.79

Descriptive statistics

Standard deviation2.1363477
Coefficient of variation (CV)0.059953818
Kurtosis-1.1195226
Mean35.633221
Median Absolute Deviation (MAD)1.23
Skewness0.46493428
Sum728093.61
Variance4.5639814
MonotonicityNot monotonic
2024-05-30T17:45:29.111784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.06 241
 
1.2%
34.08 232
 
1.1%
34.05 229
 
1.1%
34.07 227
 
1.1%
34.04 215
 
1.1%
34.09 209
 
1.0%
34.02 207
 
1.0%
34.1 201
 
1.0%
34.03 189
 
0.9%
33.93 181
 
0.9%
Other values (851) 18302
89.6%
ValueCountFrequency (%)
32.54 1
 
< 0.1%
32.55 3
 
< 0.1%
32.56 10
 
< 0.1%
32.57 18
0.1%
32.58 26
0.1%
32.59 11
0.1%
32.6 9
 
< 0.1%
32.61 14
0.1%
32.62 13
0.1%
32.63 18
0.1%
ValueCountFrequency (%)
41.95 2
< 0.1%
41.92 1
 
< 0.1%
41.88 1
 
< 0.1%
41.86 3
< 0.1%
41.84 1
 
< 0.1%
41.82 1
 
< 0.1%
41.81 2
< 0.1%
41.8 3
< 0.1%
41.79 1
 
< 0.1%
41.78 3
< 0.1%

housing_median_age
Real number (ℝ)

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.633094
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2024-05-30T17:45:29.190151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q118
median29
Q337
95-th percentile52
Maximum52
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.591805
Coefficient of variation (CV)0.43976405
Kurtosis-0.80101334
Mean28.633094
Median Absolute Deviation (MAD)10
Skewness0.061605426
Sum585060
Variance158.55356
MonotonicityNot monotonic
2024-05-30T17:45:29.284628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 1265
 
6.2%
36 856
 
4.2%
35 818
 
4.0%
16 762
 
3.7%
17 694
 
3.4%
34 682
 
3.3%
26 611
 
3.0%
33 609
 
3.0%
25 562
 
2.8%
32 560
 
2.7%
Other values (42) 13014
63.7%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 58
 
0.3%
3 62
 
0.3%
4 190
0.9%
5 242
1.2%
6 157
0.8%
7 173
0.8%
8 203
1.0%
9 204
1.0%
10 263
1.3%
ValueCountFrequency (%)
52 1265
6.2%
51 47
 
0.2%
50 135
 
0.7%
49 133
 
0.7%
48 174
 
0.9%
47 195
 
1.0%
46 245
 
1.2%
45 286
 
1.4%
44 353
 
1.7%
43 351
 
1.7%

total_rooms
Real number (ℝ)

Distinct5911
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2636.5042
Minimum2
Maximum39320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2024-05-30T17:45:29.359411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile622
Q11450
median2127
Q33143
95-th percentile6217
Maximum39320
Range39318
Interquartile range (IQR)1693

Descriptive statistics

Standard deviation2185.2696
Coefficient of variation (CV)0.82885115
Kurtosis32.713859
Mean2636.5042
Median Absolute Deviation (MAD)795
Skewness4.1588164
Sum53871691
Variance4775403.1
MonotonicityNot monotonic
2024-05-30T17:45:29.460105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1527 18
 
0.1%
1582 17
 
0.1%
1613 17
 
0.1%
2127 16
 
0.1%
2053 15
 
0.1%
1722 15
 
0.1%
1703 15
 
0.1%
1471 15
 
0.1%
1717 15
 
0.1%
1607 15
 
0.1%
Other values (5901) 20275
99.2%
ValueCountFrequency (%)
2 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
15 2
< 0.1%
16 1
 
< 0.1%
18 4
< 0.1%
19 2
< 0.1%
20 2
< 0.1%
ValueCountFrequency (%)
39320 1
< 0.1%
37937 1
< 0.1%
32627 1
< 0.1%
32054 1
< 0.1%
30450 1
< 0.1%
30405 1
< 0.1%
30401 1
< 0.1%
28258 1
< 0.1%
27870 1
< 0.1%
27700 1
< 0.1%

total_bedrooms
Real number (ℝ)

Distinct1923
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean537.87055
Minimum1
Maximum6445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2024-05-30T17:45:29.542612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile137
Q1296
median435
Q3647
95-th percentile1275.4
Maximum6445
Range6444
Interquartile range (IQR)351

Descriptive statistics

Standard deviation421.38507
Coefficient of variation (CV)0.78343213
Kurtosis21.985575
Mean537.87055
Median Absolute Deviation (MAD)162
Skewness3.4595463
Sum10990309
Variance177565.38
MonotonicityNot monotonic
2024-05-30T17:45:29.634402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280 55
 
0.3%
331 51
 
0.2%
345 50
 
0.2%
343 49
 
0.2%
393 49
 
0.2%
328 48
 
0.2%
394 48
 
0.2%
348 48
 
0.2%
272 47
 
0.2%
309 47
 
0.2%
Other values (1913) 19941
97.6%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
3 5
< 0.1%
4 7
< 0.1%
5 6
< 0.1%
6 5
< 0.1%
7 6
< 0.1%
8 8
< 0.1%
9 7
< 0.1%
10 8
< 0.1%
ValueCountFrequency (%)
6445 1
< 0.1%
6210 1
< 0.1%
5471 1
< 0.1%
5419 1
< 0.1%
5290 1
< 0.1%
5033 1
< 0.1%
5027 1
< 0.1%
4957 1
< 0.1%
4952 1
< 0.1%
4819 1
< 0.1%

population
Real number (ℝ)

Distinct3879
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1424.9469
Minimum3
Maximum35682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2024-05-30T17:45:29.714900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile348
Q1787
median1166
Q31722
95-th percentile3284.4
Maximum35682
Range35679
Interquartile range (IQR)935

Descriptive statistics

Standard deviation1133.2085
Coefficient of variation (CV)0.79526363
Kurtosis74.060888
Mean1424.9469
Median Absolute Deviation (MAD)439
Skewness4.9600165
Sum29115941
Variance1284161.5
MonotonicityNot monotonic
2024-05-30T17:45:29.794914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
891 25
 
0.1%
850 24
 
0.1%
761 24
 
0.1%
1052 24
 
0.1%
1227 24
 
0.1%
782 22
 
0.1%
1005 22
 
0.1%
825 22
 
0.1%
872 21
 
0.1%
753 21
 
0.1%
Other values (3869) 20204
98.9%
ValueCountFrequency (%)
3 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 4
< 0.1%
9 2
< 0.1%
11 1
 
< 0.1%
13 4
< 0.1%
14 3
< 0.1%
15 2
< 0.1%
17 2
< 0.1%
ValueCountFrequency (%)
35682 1
< 0.1%
28566 1
< 0.1%
16305 1
< 0.1%
16122 1
< 0.1%
15507 1
< 0.1%
15037 1
< 0.1%
13251 1
< 0.1%
12873 1
< 0.1%
12427 1
< 0.1%
12203 1
< 0.1%

households
Real number (ℝ)

Distinct1809
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.43347
Minimum1
Maximum6082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2024-05-30T17:45:29.880283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile125
Q1280
median409
Q3604
95-th percentile1159
Maximum6082
Range6081
Interquartile range (IQR)324

Descriptive statistics

Standard deviation382.29923
Coefficient of variation (CV)0.76546578
Kurtosis22.094083
Mean499.43347
Median Absolute Deviation (MAD)151
Skewness3.4138502
Sum10204924
Variance146152.7
MonotonicityNot monotonic
2024-05-30T17:45:30.118399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306 57
 
0.3%
335 56
 
0.3%
282 55
 
0.3%
386 55
 
0.3%
429 54
 
0.3%
297 51
 
0.2%
375 51
 
0.2%
284 51
 
0.2%
278 50
 
0.2%
362 50
 
0.2%
Other values (1799) 19903
97.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 4
 
< 0.1%
5 7
< 0.1%
6 5
< 0.1%
7 10
< 0.1%
8 8
< 0.1%
9 9
< 0.1%
10 7
< 0.1%
ValueCountFrequency (%)
6082 1
< 0.1%
5358 1
< 0.1%
5189 1
< 0.1%
5050 1
< 0.1%
4930 1
< 0.1%
4855 1
< 0.1%
4769 1
< 0.1%
4616 1
< 0.1%
4490 1
< 0.1%
4372 1
< 0.1%

median_income
Real number (ℝ)

Distinct12825
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8711616
Minimum0.4999
Maximum15.0001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2024-05-30T17:45:30.190899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.4999
5-th percentile1.60066
Q12.5637
median3.5365
Q34.744
95-th percentile7.30034
Maximum15.0001
Range14.5002
Interquartile range (IQR)2.1803

Descriptive statistics

Standard deviation1.8992912
Coefficient of variation (CV)0.49062567
Kurtosis4.9431411
Mean3.8711616
Median Absolute Deviation (MAD)1.0649
Skewness1.6445569
Sum79099.445
Variance3.6073072
MonotonicityNot monotonic
2024-05-30T17:45:30.290071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.125 49
 
0.2%
15.0001 48
 
0.2%
2.875 46
 
0.2%
4.125 44
 
0.2%
2.625 44
 
0.2%
3.875 41
 
0.2%
3.375 38
 
0.2%
4 37
 
0.2%
3 37
 
0.2%
3.625 36
 
0.2%
Other values (12815) 20013
97.9%
ValueCountFrequency (%)
0.4999 12
0.1%
0.536 10
< 0.1%
0.5495 1
 
< 0.1%
0.6433 1
 
< 0.1%
0.6775 1
 
< 0.1%
0.6825 1
 
< 0.1%
0.6831 1
 
< 0.1%
0.696 1
 
< 0.1%
0.6991 1
 
< 0.1%
0.7007 1
 
< 0.1%
ValueCountFrequency (%)
15.0001 48
0.2%
15 2
 
< 0.1%
14.9009 1
 
< 0.1%
14.5833 1
 
< 0.1%
14.4219 1
 
< 0.1%
14.4113 1
 
< 0.1%
14.2959 1
 
< 0.1%
14.2867 1
 
< 0.1%
13.947 1
 
< 0.1%
13.8556 1
 
< 0.1%

median_house_value
Real number (ℝ)

Distinct3833
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206864.41
Minimum14999
Maximum500001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size835.3 KiB
2024-05-30T17:45:30.378399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum14999
5-th percentile66260
Q1119500
median179700
Q3264700
95-th percentile490560
Maximum500001
Range485002
Interquartile range (IQR)145200

Descriptive statistics

Standard deviation115435.67
Coefficient of variation (CV)0.55802574
Kurtosis0.32803747
Mean206864.41
Median Absolute Deviation (MAD)68400
Skewness0.97828989
Sum4.2268606 × 109
Variance1.3325393 × 1010
MonotonicityNot monotonic
2024-05-30T17:45:30.457556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500001 958
 
4.7%
137500 119
 
0.6%
162500 116
 
0.6%
112500 103
 
0.5%
187500 92
 
0.5%
225000 91
 
0.4%
350000 79
 
0.4%
87500 77
 
0.4%
275000 65
 
0.3%
150000 64
 
0.3%
Other values (3823) 18669
91.4%
ValueCountFrequency (%)
14999 4
< 0.1%
17500 1
 
< 0.1%
22500 4
< 0.1%
25000 1
 
< 0.1%
26600 1
 
< 0.1%
26900 1
 
< 0.1%
27500 1
 
< 0.1%
28300 1
 
< 0.1%
30000 2
< 0.1%
32500 4
< 0.1%
ValueCountFrequency (%)
500001 958
4.7%
500000 27
 
0.1%
499100 1
 
< 0.1%
499000 1
 
< 0.1%
498800 1
 
< 0.1%
498700 1
 
< 0.1%
498600 1
 
< 0.1%
498400 1
 
< 0.1%
497600 1
 
< 0.1%
497400 1
 
< 0.1%

ocean_proximity
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size835.3 KiB
<1H OCEAN
9034 
INLAND
6496 
NEAR OCEAN
2628 
NEAR BAY
2270 
ISLAND
 
5

Length

Max length10
Median length9
Mean length8.0630353
Min length6

Characters and Unicode

Total characters164752
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEAR BAY
2nd rowNEAR BAY
3rd rowNEAR BAY
4th rowNEAR BAY
5th rowNEAR BAY

Common Values

ValueCountFrequency (%)
<1H OCEAN 9034
44.2%
INLAND 6496
31.8%
NEAR OCEAN 2628
 
12.9%
NEAR BAY 2270
 
11.1%
ISLAND 5
 
< 0.1%

Length

2024-05-30T17:45:30.532905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:45:30.615697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ocean 11662
33.9%
1h 9034
26.3%
inland 6496
18.9%
near 4898
14.3%
bay 2270
 
6.6%
island 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 29557
17.9%
A 25331
15.4%
E 16560
10.1%
13932
8.5%
O 11662
 
7.1%
C 11662
 
7.1%
< 9034
 
5.5%
1 9034
 
5.5%
H 9034
 
5.5%
I 6501
 
3.9%
Other values (6) 22445
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 164752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 29557
17.9%
A 25331
15.4%
E 16560
10.1%
13932
8.5%
O 11662
 
7.1%
C 11662
 
7.1%
< 9034
 
5.5%
1 9034
 
5.5%
H 9034
 
5.5%
I 6501
 
3.9%
Other values (6) 22445
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 164752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 29557
17.9%
A 25331
15.4%
E 16560
10.1%
13932
8.5%
O 11662
 
7.1%
C 11662
 
7.1%
< 9034
 
5.5%
1 9034
 
5.5%
H 9034
 
5.5%
I 6501
 
3.9%
Other values (6) 22445
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 164752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 29557
17.9%
A 25331
15.4%
E 16560
10.1%
13932
8.5%
O 11662
 
7.1%
C 11662
 
7.1%
< 9034
 
5.5%
1 9034
 
5.5%
H 9034
 
5.5%
I 6501
 
3.9%
Other values (6) 22445
13.6%

income_category
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size695.8 KiB
3
7156 
2
6515 
4
3611 
5
2337 
1
814 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20433
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 7156
35.0%
2 6515
31.9%
4 3611
17.7%
5 2337
 
11.4%
1 814
 
4.0%

Length

2024-05-30T17:45:30.699083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:45:30.761618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 7156
35.0%
2 6515
31.9%
4 3611
17.7%
5 2337
 
11.4%
1 814
 
4.0%

Most occurring characters

ValueCountFrequency (%)
3 7156
35.0%
2 6515
31.9%
4 3611
17.7%
5 2337
 
11.4%
1 814
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20433
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 7156
35.0%
2 6515
31.9%
4 3611
17.7%
5 2337
 
11.4%
1 814
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20433
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 7156
35.0%
2 6515
31.9%
4 3611
17.7%
5 2337
 
11.4%
1 814
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20433
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 7156
35.0%
2 6515
31.9%
4 3611
17.7%
5 2337
 
11.4%
1 814
 
4.0%

Interactions

2024-05-30T17:45:28.057629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:23.178471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:23.976471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.499504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.037487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.715728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.270151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.833223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.386809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:28.119788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:23.249102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.036536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.561729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.090107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.762887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.333662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.893836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.453327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:28.174296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:23.315105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.094894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.608705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.152613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.823539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.386623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.949223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.499143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:28.224236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:23.370076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.149045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.665115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.215105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.878415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.462414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.015799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.686630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:28.291027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:23.436944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.211668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.727623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.295987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.956557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.515910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.078433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.749016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:28.365112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:23.490881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.265628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.792095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.353443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.007452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.578462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.141074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.815943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:28.424331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:23.803461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.341851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.857425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.509888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.089094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.649189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.202204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.882554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:28.492079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:23.849231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.397524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.914982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.575194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.151586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.702677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.268324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.932786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:28.563965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:23.913450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.452608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:24.977486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:25.653590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.202410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:26.778415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:27.330830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:45:28.001576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-30T17:45:28.640841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-30T17:45:28.739800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximityincome_category
0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY5
1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY5
2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY5
3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY4
4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY3
5-122.2537.8552.0919.0213.0413.0193.04.0368269700.0NEAR BAY3
6-122.2537.8452.02535.0489.01094.0514.03.6591299200.0NEAR BAY3
7-122.2537.8452.03104.0687.01157.0647.03.1200241400.0NEAR BAY3
8-122.2637.8442.02555.0665.01206.0595.02.0804226700.0NEAR BAY2
9-122.2537.8452.03549.0707.01551.0714.03.6912261100.0NEAR BAY3
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximityincome_category
20630-121.3239.2911.02640.0505.01257.0445.03.5673112000.0INLAND3
20631-121.4039.3315.02655.0493.01200.0432.03.5179107200.0INLAND3
20632-121.4539.2615.02319.0416.01047.0385.03.1250115600.0INLAND3
20633-121.5339.1927.02080.0412.01082.0382.02.549598300.0INLAND2
20634-121.5639.2728.02332.0395.01041.0344.03.7125116800.0INLAND3
20635-121.0939.4825.01665.0374.0845.0330.01.560378100.0INLAND2
20636-121.2139.4918.0697.0150.0356.0114.02.556877100.0INLAND2
20637-121.2239.4317.02254.0485.01007.0433.01.700092300.0INLAND2
20638-121.3239.4318.01860.0409.0741.0349.01.867284700.0INLAND2
20639-121.2439.3716.02785.0616.01387.0530.02.388689400.0INLAND2